BACKGROUND: The traditional Bayesian priors for maximum a
posteriori (MAP) reconstruction methods usually incorporate local
neighborhood interactions that penalize large deviations in parameter
estimates for adjacent pixels; therefore, only local pixel differences are
utilized. This limits their abilities of penalizing the image roughness.
OBJECTIVE: To achieve high-quality PET image reconstruction, this
study investigates a MAP reconstruction strategy by incorporating a nonlocal
means induced (NLMi) prior (NLMi-MAP) which enables utilizing global
similarity information of image.
METHODS: The present NLMi prior approximates the derivative of
Gibbs energy function by an NLM filtering process. Specially, the NLMi prior
is obtained by subtracting the current image estimation from its NLM
filtered version and feeding the residual error back to the reconstruction
filter to yield the new image estimation.
RESULTS: We tested the present NLMi-MAP method with simulated and
real PET datasets. Comparison studies with conventional filtered
backprojection (FBP) and a few iterative reconstruction methods clearly
demonstrate that the present NLMi-MAP method performs better in lowering
noise, preserving image edge and in higher signal to noise ratio (SNR).
CONCLUSIONS: Extensive experimental results show that the NLMi-MAP
method outperforms the existing methods in terms of cross profile, noise
reduction, SNR, root mean square error (RMSE) and correlation coefficient
(CORR).
